UAV image processing: Orthorectified image

Processing UAV data

The final project consists of learning how to operate the software program Pix4Dmapper, and learning more information about the processing of UAV data. For this project, a point cloud will be constructed, which can be used later to produce a orthomosaic. The pix4D is a highly developed software program, that allows for the advanced processing of 3D datasets.

Overview of software

               
The program Pix4D gives users the ability to view, procress, and extract information from images taken using an Unmanned Aerial System (UAS). The software works by locating points in an image that are common between the two images. If the two images have a confirmed point that is similar bwtween the two images, are called keypoints. In orerder to create a 3D point, two keypoints much match.  

What is the overlap needed for Pix4D to process imagery?

For this process to be facilitated, a certain level of overlap is required. In the most general sense, Pix4D recommends at least 75% frontal overlap, with no less then 60% side overlap. If the AOI is more complex, like a dense forest, the overlap should increase to about 85% overlap in the front, and 70% on the sides.

What if the user is flying over sand/snow, or uniform fields?

Another environment which needs special consideration is if the AOI is snow, sand. These types of land surfaces have very little distinguishing content, and are very uniform. To produce a quality rendering, Pix4D suggest increasing the overlap to 80% frontal, and 70%, and adjusting the ISO/exposure settings to increase the contrast of the images. Finally, water bodies present a problem for accurate mapping, and can confuse the Pix4D programming. It is suggested that when flying a UAS over a water body, that the image contain some level of land surface to help the program calculate the surface. Pix4D states that oceans are impossible to reconstruct, because the suns refelction and the waves on the water cannot be used for visual matching.

What is rapid check?
This processing extent allows the user to process the data quicker, but the increase in processing speed is comes with a decrease in image quality and accuracy. Rapid check would is useful for quick calculations where accuracy and image quality are not important.

Can Pix4D process multiple flights? What does the pilot need to maintain if so?

 The Pix4D also allows the user to fly multiple flight paths. For this, as much of the information must be kept as constant as possible. Variables like flight height at image capture, general time of day (suns position, shadows), weather conditions (sunny vs cloudy) and contain as much overlap as possible, at minimal 8-% frontal, and 60% side overlap. The imaging software also allows users to process oblique images, but recommends the maximum amount of overlap, with multiple flights, varying the distance above ground with each flight to gain the most amount of information about the terraign.

Are GCPs necessary for Pix4D? When are they highly recommended?

 For the production of a 3D image, many other programs require the prescence of ground control points (GCP) to essentially ‘tie down’ the image to a real ground surface, and dramatically improving the accuracy. In Pix4D, GCP’s are not inherently required to produce ortholog images, but are highly recommended. This cannot be understated how highly GCP’s are recommended. For this project, as an informative guide to learning the Pix4D software, no GCP will be used for the processing of the images.

What is the quality report?

One thing that is very helpful with Pix4D, is the program produces a ‘quality report’, which entails a great deal of information about the images that are going to be processed, and if anything in particular stands out that may produce an error, or inaccuracy in the mosaic image. The quality report informs the user of the details of how the images are processed, and gives a quick preview of what the data may look like.

Methods

               
For the purposes of this lab, the instructor Dr. Hupy provided the class with UAV imagery that was taken from flights performed at a sand mining facility a few miles south of the City of Eau Claire. First, the data is transferred into the students folders. The complimation of images is then imported into the Pix4D mapper by connecting to the folder. Once the images are imported into Pix4, the AOI is specified, the flight path can be visualized on the main Pix4D mapper view. For this project, a AOI is created inside the image by specifying the extent of the processing, and hand drawing a polygon of the desired area. Next, the images can be processed by clicking on the processing tab in the bottom right of the screen. To save on processing time, the initial processing can be ran individually, instead of running the entire process at once. Once the initializing processing has taken place, the quality report is derived from the information about the images. The quality check provides a detailed report about the specifics of the images.

Figure 1: Summery of the initial quality report

Importantly, is the initial summery which gives the basic information about, like the camera used, the sampling distance and project name. The quality check then describes the Images, dataset, camera optimization, matching, and georeferencing.

Figure 2: Preview of the dataset image mosaic
provided by the quality report.

More information about overlap extent, and an image preview is produced, and can inform the user about any problems that may occur in further processing. Figure 3 displays an image provided by the quality report that shows the areas of overlap between the images. The areas of red and yellow have relatively poor overlap, and the area in green is a high level of overlap. The edges of the image are the areas with the least overlap, and may have a poor quality of the image. Its important to keep in mind the area the study is interested in, and make sure the entirety of the area is in high image overlap.

Figure 3: Areas of image overlap

If all the quality check criteria are green, then the user proceeds to the next step of processing, which is production of a point cloud mesh, a raster DSM, and finally an orthomosaic. All of these steps are processed by the computer, by clicking on the processing button, same as before. Once the project is complete, further processing can be done to explore the power of the pix4D mapper.

Results

The final product of an ortholomosaic can be visualized before once the processing has been completed. An orthomosaic can produce many functionally useful products. One of which is the ability to measure the volume of a given object. 

Figure 4: Orthomosaic image

The orthomosaic image is a spatially accurate image, and can be used to calculate volumes of 3D objects. Using a volumetric measuring tool in Pix4D, the volume of one of the mounds is calculated. This powerful tool is a great analysis tool that can add a great deal of quality data to any project.

Figure 5: Volume calculation using Pix4D. Calculating the volume of the
mound highlighted in red. 

A final product of this project, is a video 'fly by" of the orthomosaic image that was made previously. The program in Pix4D facilitates the collection of images that are compiled together to create a video where the viewer gets a tour of the image created in 3D space.

Conclusion

This project offered a quick, and easy way to get an introduction to working with UAV datasets, and the Pix4D mapper program. The Pix4D presents a great deal of complex methods to process and visualize 3D data, and is a dynamic tool that can be used to enhance the quality of a project. Many of the techniques of processing are dynamic, and can be used in many other programs like Esri ArcMap. Although this lab worked through the process of creating a orthomosaic image, Pix4D offers many more capabilities and output processing methods.

Field Survey

Introduction

The project for this week’s field exercise involves taking data points a highly accurate GPS receiver. The goal of the project is to produce a continuous surface that illustrates the elevation change in a small patch of grass on the University of Wisconsin Eau Claire campus mall. To facilitate the collection of data points in the small area, a survey grade GPS is utilized to collect data with accuracy close to a centimeter. Survey GPS offers an incredibly accurate source of data collection that can be utilized to carry out a field survey. Like any advanced technology, there are a few downsides. Survey grade GPs units are often very expensive, and can be rather cumbersome. For this exercise, the entire class had to take turns working in pairs to collect points, because the university only owns one Survey Grade GPS unit. For many, this was not the first time using the Survey GPS. The GPS was also used during the mapping of the Hadleyville Grave yard to gather data on the head stone locations.

Study Area

Figure 1: Blue represents the study Area on the
University of Wisconsin Eau Claire Campus

            The area of interest for this project is a small portion of grass that is located in the middle of the University of Wisconsin Eau Claire campus mall (Figure 1). The grass is surrounded by sidewalk, and contains 3 small trees, and 3 benches. The area is located a decent distance away from any large buildings, so there should be little expected distortion of the GPS signal, especially since the collection will be carried out on a Survey grade GPS. The grass area has a gentle curve in elevation, with the lower portions of the area located on the northern portion, and the higher elevation located in the southern portion.

Methods

            To collect the data, the survey grade GPS unit (receiver and tablet) was deployed to the location on campus. Since there was only one unit to offer, students in pairs of two took turns setting up the receiver in different locations and collecting data. The grass area was sampled using a random sampling method. The collection period was limited to 20 points.

Once data collection was complete, the text file is brought into the computer. The text is then converted to an excel file, along with a changing in the attribute headings for import into Esri's Arc maps. To display the now XY data, the Excel file is changed to table and added to the map using the ‘Display as XY data’ tool. The data point are projected into UTM Zone 15N, so fit the study area.

To complete the project goal, 5 different interpolations were completed to illustrate a continuous surface of the elevation of the grass area. The techniques used are Inverse Distance Weight (IDW), Kriging, Natural Neighbor, Spline, and a TIN surface.

Results

 IDW

Figure 2: IDW Interpolation

The equation for the Inverse Distance Weighted technique takes the cells closely surrounding a sample cell at a greater weight mathematically then cells further away from the sample cell. This method provided a very real to life representation of the slope of the grass area (Figure 2). The high point of the area is located fairly central, in the south east corner. The lowest points are located along the western border.

Kriging

Figure 3: Kriging

The Kriging method uses the height attribute to calculate the continuous surface. The data we collected, the Kriging did a rather poor job of capturing the slope and direction of the grass area (Figure 3). The interpolation over generalized the high point, and the relief in the northwest corner of the surface.

Natural Neighbor

Figure 4: Nearest Neighbor Interpolation

The Nearest Neighbor Technique uses a proportionality to take cells surrounding a sample point to be weighted differently. This technique is quite complex and involved a fair amount of modification (Figure 4). This is probably a reason behind the product being a complex representation of the actual surface of the grass area.

Spline

Figure 5: Spline Interpolation

The technique using spline interpolation uses a mathematical formula that creates a very smooth output by estimating the minimal curve of the elevation points. The spline interpolation did give a very smooth output, but over compensated and added a seeming bump in the slope in the west central side of the surface that is not present in the actual grass area (Figure 5). The spline technique also placed the high point to far in the southeastern corner then it presents in actuality. The technique would improve with further customization and more data points.

TIN

Figure 6: TIN 

This method produced the best result for the illustration of the elevation of the grass area. The TIN method did not capture the relief with complete accuracy (Figure 6). Like the Nearest Neighbor technique, the TIN tool is very programmable and the result could change quite a bit with more investment of manipulating the tool parameters.

Conclusions

The products of the interpolations from the data collected with the survey grade GPS did not accurately reflect the actual surface of the grass area that was surveyed. There could be several different reasons for the lack of strength in the final product. Initially, the sampling method was carried out in a disorganized fashion by students, which resulted with no information being captured in the central western, and north eastern portions of the grass area Even though the sampling method was ‘random’, some care could have been taken to assure that at least some points for every area is collected. Another issue encountered was the data was originally uploaded from the GPS unit text file as UTM ZONE 16N, not UTM 15N (faculty error). The error was rectified quickly and replaced with the correct coordinate system UTM 15N, and placed into a temporary drive to be accessed by students. However when the time when the interpolations were done the Temporary folder was empty. The only file left was the original upload, which was in UTM Zone 16N. This meant that a basemap was unable to be loaded behind the surface to provide context to the results. More organization at all levels could have increased the accuracy of the results.

Arc Collector Part Two

A Campus Tree Species Catalog;

A University of Wisconsin Eau Claire Student-Faculty Project

Background:

            In an initiative to facilitate the growth of the University of Eau Claire campus, the University plans to plant 100 trees in the institutions centennial year (2016-2017).  The campus currently has 83 identified different varieties of tree species, and with the new additional trees being planted that number will grow to 107 species. The addition of new tree species is an effort to harbor the idea of sustainability, and the further inclusion of the ‘urban’ environment into the ‘natural’ environment. The University of Wisconsin Eau Claire is investing considerable effort into cultivating an interactive arboretum over the entirety of the campus.

https://www.uwec.edu/news/news/planting-ceremony-to-launch-centennial-100-trees-project-at-uw-eau-claire-1813

           

The campus arboretum student-faculty collaboration, that has gained much support from the university and surrounding population. The need for a catalog of the population size and species becomes very important for the development of a sustainable and ecologically diverse environment on campus. In order to gain knowledge of the tree species located on campus, a survey must first be conducted. The project entails the careful attention to the species, and the population of each species on campus. Given that the project is inherently geographic, initiating a geographically based product for future development presents a great pathway. To facilitate the production of the map, the online version of Esri’s ArcMap is implemented using the mobile version of a collection application called Esri ArcCollector.  http://doc.arcgis.com/en/collector/

The ArcCollector app allows for the creation of map that is capable of being published to an online platform, where the collection of data can be captured using a mobile phone device in the field. The centennial year project mark the beginning of a GIS-based map project that will be an ongoing categorical tool used by University faculty, and will be maintained and updated by faculty and student help in future semesters. The project is portioned out into two parts, producing separate maps. The preliminary portion of the Campus Arboretum mapping project was created by Martin Gotle (University Geographic Information Systems), with help from Daria Hutchinson (University Master Gardener). The map created is a locator map, identifying one of each type of species present on the campus currently. The long-term plan for the map is to be a publically accessible platform for people visiting the campus to participate in the wonderful nature that encompasses the University of Wisconsin Eau Claire. The potential end goal of this map is to make an “Arboretum Walking Tour” that is guided by the individual’s mobile phone as they walk around the campus. An addition to that, signs will be located in front of particularly magnificent trees around the campus, containing a QR code can be scanned to give more information about the species, genius, and history of the tree. The second portion of the project is the creation of a large database of every tree located on campus. This map and database will be purpose built for campus faculty, particularly the grounds department for the planning, maintenance of the trees located on campus. The mass collection of data points can be used as a geographic tool to keep track of and manage the species that are located on the campus. This type of map will be useful for tracking the progression of tree diseases, the presence of pests/bugs, and damage control after strong winds or storms. As the campus continually grows with new construction, a highly detailed database of the trees located on campus will become more important as greater details of the renovations and construction that is being completed.

Study Area

The University of Wisconsin Eau Claire is situated directly next to the Chippewa River, in West Central Wisconsin. The campus is located directly adjacent to Putnam Park, in the city of Eau Claire. The University of Eau Claire campus is expanding and growing, and is currently in the process of several major construction projects.

The university campus contains also several areas of dense forested area. For the constraints of time, and data space, this project will not include these areas in the study. For the project, a sampling area created was subdivided into separate zones. These zones use clean boundaries like roads and paths where no trees will grow to delineate the separate zones. In sections of the area where there is no clean boundary, specific use of a fence line, of building boundary is used to delineate the separate zones as described in the “Zones” sections of this report. The sampling area includes the satilite location of Bollinger Fields (not shown) and the HAAS Fine Art Center and accompanying parking lot.

Figure 1: Zones for sampling tree species populations on the
University of Wisconsin Eau Claire Campus.

Methods

            Completing a mapping project requires a large portion of planning and for-thought to produce a successful product. Often times, using the technique of reverse engineering the project leads to the most effective workflow. Starting with a solid idea of what the end product should looks like, and how it is used can greatly increase the effectiveness of the project. For the Arboretum Tree Catalog map, much of the preliminary work entailed deciding how to structure the map so the data can be curated online in a ordered fashion.

Figure 2: Table displaying the domain specifications
for the attribute field "Tree Genus"

Similarly to any project utilizing a map, the creation of a geodatabase where all map layers and data can be stored. In the geodatabase, a new feature class is created. The new feature class will possess attribute fields of important information (like tree name, genus, maintenance …etc.). Each field will have a specific domain created, to facilitate greater data integrity and fewer mistakes when collecting data in the field. For the Arboretum Tree Catalog project, the domains are structured to reduce the human error of entering information in wrong. Most of the fields are text files for the name and genus of the tree identified. Several fields are set up to be a Y/N selection, for example there are two trees on campus that are considered to the university to be Heritage trees. A field was created for this attribute, and to accelerate the collection procress was made to be a Y/N selector. After the field type is configured using domain, a map is created in the desktop form of ArcMap. The map is built containing a shape file with the sampling zones that can be digitized in using a basemap for reference. As an added benefit to this project, Martin Gotle (GIS supervisor UWEC) previously supplied detailed basemap information directly to Esri about the University campus. The added level of cartographic detail helped create a better basemap to digitize the sampling zones more effectively. Details like added fence lines, and updated building plans made designating boundary lines less ambiguous then if the digitizing was completed using a satellite image, as sometimes in the satellite image trees can block certain physical elements, or the data may not be recent. Once the zones are established, and the feature class is created, the map can be published as a service and can be accessed from the Universities online Esri profile. Following the specifications of the publishing wizard, the map is made available to be accessed by any invited party.

Using a mobile device, the ArcCollector app is downloaded and installed. Opening the application and signing into the ArcGIS online page, the collection of a data can be done using the Collector app.

Results:

The preliminary results for the tree species population database collection are displayed above in an embedded map. The caragorization of the trees that are present is going to be an ongoing collection of data points, with an end goal of mapping each of the trees located on the campus.

Conclusion:
 The utilization of the mobile format of ArcCollector with the strength of a geodatabase allows for a very dynamic and streamlined mapping process. This mapping project is the beginning of a multi- year project that will expand the understanding about where trees are located specifically on the university campus. This geographic attribute map will provide an major roll in how effectively and comprehensively wish to keep the goal of sustainability and growth as the campus and university expand into the future.  

Arc Collector

Introduction

            The current state of technology has allowed for some ingenenious advancements. Computing devices have become small and compact, and are combined with more versatile abilities, opening up many new aveneus of data collection. Most people walk around with mini computers in their pockets that are capable to processing enormious amounts of data. These pocket compters, cell phones, have given geographers an unprecedented ally to be mobile and collect data. The cell phones also carry onboard an (fairly) accurate GPS, for use with navigation. Untilizing both of these advancments, the geographers and computer programers at Esri have found a way to use the cell phones interface computing abilities, screen, and GPS to produce a mobile app that allows a user to implement a project online, and access it through the devices data connection.

            The task for this project, is to gain a familiarity with Esri ArcCollector application, and produce several different micro-climate maps of the University of Eau Claire campus. For the means of this introductory project, the featureclass domains where constructed by the instructor. The different types of information for the collection process includes; wind-speed, wind direction, temperature, and dew point.

Study Area

            The area of interest for this project, is the University of Wisconsin Eau Claire campus. The campus is located in the city of Eau Claire, directly adjacent to/containing portions of the Chippewa River. The campus features environmental factors like a forested area, several fields, and a steep inclining slope acting as a boundry between upper and lower campus. On the day of collection, the date was November 9^th, and collection was carried out from 3:30pm – 5:30 pm. The temperature was an average of 58 degrees F. The assigned zone for collection for group 7 was zone 5. 

Methods

 

Figure 1: Image of devices used to collect data.
From left to right, Samsung Smartphone,
Weather Device, Compass. 

            The data is collected in points using cellular devices, employing the ArcCollector application. Using the phones GPS units, points can be collected within a spatial accuracy of between 10-15ft. A weather device was used to collect wind speed, dew point, and temperature at the time of each reading. For the same data point, a compass was used to note the direction of the wind (where it was coming from). The end product of this project resulted in 3 maps being produced. A microclimate temperature map, dew point map, wind speed and wind direction maps. The data points were interpolated into a continueous surface using the IDW technique, which assumes cells that are closer should be more heavily weighted to be a similar quantity. For this interpolation, the power was increased to 4 because the AOI is realativly small, and the cell search radiious neightbor hood was decreased from the standard to further define the mirco climates that may exist on the campus. All the maps produced are displayed on top of a arial image (latest year taken) of the study area.

Results

A. Temperature is represented in the surface as a spectrum of red/brown to teal blue. Red/brown represents the high end of the temperature specture, and teal blue to low.

Figure 2: Campus map of Temperature.

B.

Dew Point

Dew point was a maesurment that was collected, in aim to make a visual distinguishing mark between cement and concerte areas, and grassy or forested areas.  For this map, IDW interpolation was used with the same characteristics as specified before.

Figure 2: Campus map of Relative Dew Point

C.

Wind speed and direction was taken with each point collection. The direction was taken using an azimuth direction on a compass.

Figure 3: Campus map of wind speed and wind direction

Discussion

            This project is a great example of how the versatile field of geography finds new solutions to the age old task of how to collect data. The ability to use a previously existing computational platform (cell phone), and apply a online collection platform, allows users to compile vast amounts of data remotely, utilizing the cells phones screen, GPS, and hardware. This platform fit this project perfectly, allowing a class full of young geographers collect data simultaneously to compile a dynamic map displaying the findings. The collection process was facilitated by a previously compiled map project, with domains controlling the acceptable answers for each of the categories. The output maps are a product of a data collection process that is very much the future of technical geography, and a valuable skill to learn.

NAVIGATION

Introduction:

The technical skill of being able to navigate using a compass and topographic map is a universal ability. As geographers especially, the tried and true method of using a compass to direct to another location is a valuable skill, and should form the foundation of any field applied geography technique. Even if there are no professional reasons, having the ability to read a map, and accurately direct yourself or others to a location can be a vital skill. This is increasingly important for any trail hiking, or backcountry excursions where cellular service will be limited. The analog format of map provide an excellent reference, and can be very useful if designed properly.

            The task at hand for this project, is to assess the navigation maps that were made in the previous week’s project. To do this, each group was given a selection of 5 points that were mapped out in various locations within the University of Wisconsin Eau Claire’s land, called the Priory. Using a navigation map, a compass, and several techniques learned during lab, groups were asked to find each of the locations. Each group was also given a GPS, to track the path that each group took, and to produce a map after completion of the task to assess how the terrain and environmental factors (dense forest) affected the path of each group.

Figure 1: Image of the navigation map used,
and the general placement of the
compass for navigation in the field.
Image Credit: Oliver W. Larson

Methods:

(Group 2; Marcus, Hannah)

------Navigation Project--------

Start time 3:30, End time 5:30

60 degrees F

Sunny

11.02.2016

            After the groups arrived at the Priory, they were given the coordinates to their locations. The groups then separated and plotted the locations by hand on a printed, physical, copy of the navigation map. Only one map was chosen (per 3 group members), and for our group Hannah’s was selected. Her navigation map consisted of an aerial image, with a 50m contour line over laid on top. A 50-meter grid was applied. The map that was used more frequently for the actual navigation was using a coordinate system based on UTM.

            Before leaving for the actual navigation, a pace count was needed to be able to track distances in the field. A 50-meter length was measured in the parking lot. Students paced out the length and came up with an approximate estimate of 50 meters using a pace count. My pace count was 30-32 paces in 50 meters. A pace is counted every right-legged step.

Figure 2: Image of a compass similar to the one
used for this assignment. Image was captured
from nhtramper.wordpress.com

            Next, students were given a quick lesson in how to operate a compass, and use it for a direction. The technique that was taught is called “Red in the Shred”, and is completed as follows. While holding the compass in front of you, the direction of travel arrow is turned to point in the direction that is desired to travel. Once the arrow is pointing towards the desired location, the user should turn his/her entire body until the red portion of the magnetic needle is inside of the red outline for the orienting arrow. Hence, keeping red in the shed. As the user travels, he/she should keep the red arrow in the red outline to continue travelling on course. 

Figure 3: Image displaying the density of the forest covered
during the navigation project.
Image credit: Oliver W. Larson

This however, can get very challenging when navigating in dense wooded areas. A method to help navigate through dense woods is using a landmark in the distance that is in the direction of travel. Once a landmark has been picked out that is confirmed in the direction the user needs to travel, the user can zip-zag around obstacles, as long as he/she reaches the landmark point successfully. Using these techniques, the groups navigated to each of the points that have been selected for each group.

As it can be seen in Figure 3, the forested area consisted of dense brush. This made navigating through the area very difficult, even using the landmark method. Just moving about from location to location provided much difficulty for our group. The dense brush altered our course several times, so a Trimble June GPS (Figure 4) was used as an aid to help find some of the more challenging marks.

Figure 4: Trimble Juno GPS unit, showing the track log
on screen with objective coordinates on the paper.

Discussion

            This project called for groups of students to navigate to different locations around the Priory property, supposedly following the straight-line paths, as directed by using a compass to follow a certain azimuth for a certain distance. This is the ideal process, but in reality, the paths that the groups took needed to account for dense forested areas, that were too challenging to pass through “as the crow flies” (straight-line). Our group originally tried to venture through the dense forested area, using the navigation technique of picking landmarks, and counting the pace, but quickly gave up on this venture, as the landscape was very challenging to cross. 

Figure 5: example of the mark
supposedly at each location. 

The group soon became quite off course, and was unable to find an object in the map to orient themselves off of. The group then made a path to an access road that shadowed the forested area, and used the access road to walk closer to location 1.  Using the map, and landmarks like large trees, the group soon found the first location. This similar type of problem occurred en route to location 2, and location 5, which were never formally found. As the track log shows ( Figure 6), the group was quite close to location 5, and 2. As an additional note, the pink ribbons have been known to be removed by other (thinking they are hunting marks), so it is entirely possible that the ribbons were removed. The group is confident in the locations that were visited were in the generally right location for the ribbons. With the exception of location 5, which was in an area with a large amount of downed trees that made travel very difficult. As the track log shows, open areas and paths previously created provided a much better travel path, that was used for the majority of the navigation in the project. 


Conclusions

Overall, this project was vey informative, and provided and excellent foundation into navigation using a compass. Many of the problems that occurred were able to be quickly fixed or corrected while in the field, and a successful navigation was made to (most) of the assigned locations. This project is an fantastic example of the preparation and techniques that are required to navigate from location to location, given an adverse terrain.